Tina Eliassi‐Rad
- Artificial Intelligence top 0.2%
- Statistical and Nonlinear Physics top 0.2%
- Computer Vision and Pattern Recognition top 1%
- Information Systems top 0.5%
- Computer Networks and Communications top 2%
- Co-authors
- Brian GallagherMustafa BilgicLise GetoorGalileo NamataPrithviraj SenChristos FaloutsosHanghang TongLeman Akoglu
- Topics
- Complex Network Analysis Techniques (44 papers)Advanced Graph Neural Networks (27 papers)Network Security and Intrusion Detection (11 papers)
- Partner nations
- United StatesNetherlandsUnited Kingdom
In The Last Decade
Tina Eliassi‐Rad
98 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Artificial Intelligence 3.0k
- Statistical and Nonlinear Physics 1.9k
- Computer Vision and Pattern Recognition 861
- Information Systems 847
- Computer Networks and Communications 709
Countries citing papers authored by Tina Eliassi‐Rad
This map shows the geographic impact of Tina Eliassi‐Rad's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tina Eliassi‐Rad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tina Eliassi‐Rad more than expected).
Fields of papers citing papers by Tina Eliassi‐Rad
This network shows the impact of papers produced by Tina Eliassi‐Rad. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tina Eliassi‐Rad. The network helps show where Tina Eliassi‐Rad may publish in the future.
Co-authorship network of co-authors of Tina Eliassi‐Rad
This figure shows the co-authorship network connecting the top 25 collaborators of Tina Eliassi‐Rad. A scholar is included among the top collaborators of Tina Eliassi‐Rad based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tina Eliassi‐Rad. Tina Eliassi‐Rad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 38 | |
| 6 | 11 | |
| 7 | 62 | |
| 8 | Geometric Laplacian Eigenmap Embedding. | 0 |
| 9 | 27 | |
| 10 | 17 | |
| 11 | 46 | |
| 12 | Continuous Time Group Discovery in Dynamic Graphs | 2 |
| 13 | DAPA-V10: Discovery and Analysis of Patterns and Anomalies in Volatile Time-Evolving Networks | 4 |
| 14 | PaCK: Scalable parameter-free clustering on K-partite graphs | 7 |
| 15 | 34 | |
| 16 | Finding Mixed-Memberships in Social Networks. | 16 |
| 17 | 1 | |
| 18 | Statistical Modeling of Large-Scale Scientific Simulation Data | 1 |
| 19 | A Theory-Refinement Approach to Information Extraction | 10 |
| 20 | Building intelligent agents that learn to retrieve and extract information | 7 |
About Tina Eliassi‐Rad
Tina Eliassi‐Rad is a scholar working on Statistical and Nonlinear Physics, Computational Mathematics and Artificial Intelligence, having authored 106 papers that have together received 4.6k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (44 papers), Advanced Graph Neural Networks (27 papers) and Network Security and Intrusion Detection (11 papers). The work is most often cited by research in Statistical and Nonlinear Physics (1.9k citations), Artificial Intelligence (3.0k citations) and Computational Mathematics (46 citations). Tina Eliassi‐Rad has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Brian Gallagher, Mustafa Bilgic, Lise Getoor, Galileo Namata, Prithviraj Sen, Christos Faloutsos, Hanghang Tong, Leman Akoglu, Keith Henderson and B. Aditya Prakash. Their work appears in journals such as Nature, Nature Communications and Journal of Neuroscience.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.